Low-level wind shear grading identification and early warning method based on doppler weather radar

Abstract

The application discloses a low-level wind shear grading identification and early warning method based on Doppler weather radar, which comprises the following steps: identifying convergent shear sections and divergent shear sections; identifying convergent areas and divergent areas; matching the convergent shear sections with the divergence cores of the convergent areas to form convergent wind shear clusters; matching the divergent shear sections with the divergence cores of the divergent areas to form divergent wind shear clusters; clustering the remaining shear sections to obtain convergent shear clusters or divergent shear clusters located in non-divergence core areas; respectively extracting parameters of various shear clusters and grading; extracting the boundaries of the graded shear clusters or the boundaries of the divergence core areas, generating early warning areas and outputting and displaying. The application uses Doppler weather radar data, can accurately obtain near-surface wind shear structures, and identifies the occurrence positions and intensity changes of the divergent or convergent wind shear. The method still has high reliability under complex weather conditions.

Description

Technical Field

[0001] This invention belongs to the research field of atmospheric science (remote sensing data analysis), specifically involving a method for graded identification and early warning of low-level wind shear based on Doppler weather radar. Background Technology

[0002] The movement and changes of airflow in the lower atmosphere are inextricably linked to various human activities. Wind shear, caused by strong convection or topography, has a significant impact on human production activities. Wind shear is a common atmospheric phenomenon, referring to the rapid change in wind direction (or) speed over a short distance at the same or different altitudes. Based on the different changes in wind direction, wind shear can be divided into horizontal wind shear, vertical wind shear, and vertical wind shear. Wind shear occurring below 600 meters in flight altitude, primarily during aircraft takeoff and landing, is called low-altitude wind shear.

[0003] In areas such as airport runway takeoff and landing areas and drone flight paths, strong wind shear can cause changes in the headwind or tailwind encountered by aircraft. If a loss of headwind occurs (divergent wind shear), the aircraft's lift will decrease, causing it to deviate from its intended flight path or even crash. Conversely, if a strengthening headwind occurs (convergent wind shear), the aircraft's lift will increase, potentially allowing it to fly higher than its intended path, affecting takeoff and landing. This highlights the importance of low-altitude wind shear identification and early warning methods in wind-sensitive areas such as airport runways and low-altitude drone flight zones.

[0004] Atmospheric wind shear is affected by a variety of factors, making it difficult to detect. In particular, some detection equipment may be affected when rainfall or strong convection occurs, resulting in serious deficiencies in the accuracy and coverage of low-altitude wind shear detection and early warning that could affect aviation safety in low-altitude areas. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems existing in the related art.

[0006] One objective of this invention is to provide a method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar. By combining radial velocity data obtained from Doppler weather radar observations with a wind shear inversion algorithm, the near-surface wind shear structure can be obtained more accurately, and the location and intensity changes of divergent or convergent wind shear can be identified. This method can maintain high reliability even under complex weather conditions, providing important support for aviation safety.

[0007] To achieve the above objectives, the present invention provides a method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar, comprising the following steps:

[0008] S1. Based on the low-level elevation angle radial velocity data detected by Doppler weather radar, an adjustable sliding window is set, and the window threshold method is used to slide and extract all velocity shear segments at each azimuth angle from near to far along the radial direction. This yields convergent shear segments with monotonically decreasing radial velocity and divergent shear segments with monotonically increasing radial velocity. The radial velocity shear magnitude and shear length of each shear segment are then calculated.

[0009] S2. Calculate the two-dimensional divergence field using the same low-level elevation angle radial velocity data. Divide the identified two-dimensional divergence field into convergence regions with positive divergence and divergence regions with negative divergence. Then, extract divergence cores in the two types of regions according to the set intensity classification and obtain the two-dimensional region boundaries of each divergence core.

[0010] S3. Match the convergent shear segment with the divergence core of the convergent region to form a convergent wind shear cluster; match the divergent shear segment with the divergence core of the divergent region to form a divergent wind shear cluster.

[0011] S4. For the remaining shear segments that have not been matched with any divergence core, perform two-dimensional clustering based on the shear segment azimuth angle and radial overlap distance to obtain convergent shear clusters or divergent shear clusters located in the non-divergence core region.

[0012] S5. Extract the wind shear size, region area, and azimuth parameters of each type of shear cluster, and classify each shear cluster.

[0013] S6. Extract the boundary of the graded shear cluster or the boundary of the divergence core region, and use a two-dimensional closed graph fitting method to generate the warning area and output it for display.

[0014] A further preferred embodiment of the present invention is that step S1 specifically comprises:

[0015] Low-level elevation radial velocity data detected by Doppler weather radar are processed in polar coordinates, and the acquired data is first subjected to quality control.

[0016] Based on the radar resolution, a sliding window of length N is defined and slids radially from near to far at each azimuth angle. It is determined whether the data within the window is a shear segment with a monotonically increasing or decreasing radial velocity. After multiple windows are identified as monotonically increasing or decreasing, these regions are defined as shear segment regions. Among them, the radial velocity monotonically decreasing shear segment region is a convergent shear segment, and the radial velocity monotonically increasing shear segment region is a divergent shear segment.

[0017] Obtain the position information of all shear segments in polar coordinates at each azimuth angle, and calculate the radial velocity shear magnitude and shear length of each shear segment; remove shear segments that do not meet the conditions, and store the two types of shear segments separately by number.

[0018] As a preferred option, step S2 specifically involves:

[0019] Low-level elevation radial velocity data detected by Doppler weather radar are processed in a polar coordinate system, where radial velocity is taken... 1 grid point, as the length; take 1 grid point in the azimuth direction. Using a grid of points as the width, this region is the two-dimensional divergence region. When calculating the two-dimensional divergence of this region, the effective data rate of this interval needs to be considered. Function representation. Taking the center coordinates of this region as the origin, the... The radial velocity of each grid point is Calculate the single-layer radial velocity of radar detection for any number of layers. Two-dimensional divergence at each grid point , can be represented as:

[0020] ;

[0021] in, For follow The coefficients vary depending on the situation. It is the azimuth angle. for Distance to the origin This is a function to check the validity of radial velocity data in a two-dimensional region. It checks if the number of valid data points in the two-dimensional interval exceeds a given threshold (the default threshold is 1). (Can be adjusted according to user needs) Definition To be credible, otherwise this point Defined as a non-trusted value;

[0022] After processing all radial velocities in a single layer, two-dimensional divergence field data in polar coordinates is obtained. The divergence field data is then interpolated into Cartesian coordinate data centered on the radar station. The interpolated data is divided into two cases: convergence region with positive divergence and divergence region with negative divergence. The two regions are then classified according to the magnitude of divergence. The divergence core region is extracted, and the boundary coordinate set of the divergence core region is extracted using a boundary extraction function.

[0023] As a preferred option, step S3 specifically involves:

[0024] The position information of the convergent shear segment and the divergent shear segment obtained in step S1 is transformed from the polar coordinate system to the Cartesian coordinate system;

[0025] Using the shear segment-divergence intensity center closed region position overlap matching method, the convergent shear segments are matched one by one with the divergence core of the convergence region obtained in step S2, and the convergent shear segments matched with the same divergence core of the convergence region are clustered into convergent wind shear clusters.

[0026] The divergent shear segments are matched one by one with the divergence cores of the divergence region obtained in step S2, and the divergent shear segments that match the same divergence core of the divergence region are clustered into divergent wind shear clusters.

[0027] As a preferred option, step S4 specifically involves:

[0028] After the matching in step S3, convergent wind shear segments or divergent shear segments that fail to match any convergence or divergence core are subjected to cyclic two-dimensional clustering according to the shear segment azimuth threshold standard and radial overlap distance standard until all shear segments have been clustered and matched. Finally, convergent shear clusters located in non-divergence core regions and divergent shear clusters located in non-divergence core regions are obtained.

[0029] As a preferred option, step S5 specifically involves:

[0030] Based on the convergent and divergent wind shear clusters obtained in step S3, and the convergent shear clusters in the non-divergent core region and the divergent shear clusters in the non-divergent core region obtained in step S4, after numbering, the wind shear magnitude, region area and azimuth number parameters of each shear cluster are extracted, and the classification is carried out according to the radial velocity wind shear intensity or according to the two-dimensional divergence intensity field.

[0031] As a preferred option, step S6 specifically involves:

[0032] For shear clusters classified according to radial velocity wind shear intensity, the starting position point of each shear segment in each shear cluster and the entire coordinate point of the shear segment located at the starting azimuth angle of the shear cluster are extracted as boundaries, and a two-dimensional closed graph is used for fitting.

[0033] For shear clusters graded according to a two-dimensional divergence intensity field, the boundaries of the divergence core grading regions of the two-dimensional divergence field are extracted and fitted using a two-dimensional closed graph.

[0034] In another aspect, the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, which cause a computer to execute the aforementioned method for classifying and identifying low-altitude wind shear based on Doppler weather radar.

[0035] In another aspect, the present invention provides an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus, and the processor calls logical instructions in the memory to execute the aforementioned low-altitude wind shear classification identification and early warning method based on Doppler weather radar.

[0036] In another aspect, the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, the computer executes the aforementioned method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar.

[0037] This invention proposes an innovative and highly practical method for low-altitude wind shear identification and early warning in Doppler weather radar, particularly effective during rainfall or severe convection. It addresses the problem of insufficient low-altitude atmospheric wind shear identification and early warning capabilities that could impact aviation safety during rainfall or severe convection. Compared to existing technologies, it offers the following advantages:

[0038] (1) This invention uses Doppler weather radar radial shear segment data, combined with the shear segment clustering of two-dimensional divergence intensity core into shear clusters and classifying them. This method can obtain the actual wind shear intensity level and its spatial distribution in the low-altitude atmosphere, which can make up for the current lack of observation of the low-altitude wind shear intensity level. According to the user's needs for the wind shear level they are interested in, the corresponding wind shear level products can be calculated and extracted to carry out graded early warning, thereby improving the refined monitoring capability of low-altitude wind shear.

[0039] (2) This invention provides a clustering method that first uses a two-dimensional divergence field to extract the core, and then matches the shear segments with it to form shear clusters, whereas current conventional algorithms only use shear segment clustering. This approach may miss shear segments due to data quality issues, leading to inaccuracies such as clustering the same shear cluster into two or more shear clusters, or clustering multiple adjacent shear clusters into one, often resulting in high false alarm and false detection rates. The method of this invention effectively avoids this problem, enabling the clustering of shear segments belonging to the same core into the same shear cluster, and also accurately clustering shear segments belonging to different cores into separate shear clusters. This method improves the accuracy of low-altitude atmospheric wind shear clustering and the ability to monitor low-altitude atmospheric motion.

[0040] (3) This invention uses real-time detection data from Doppler weather radar, which is applicable to various bands (S-band, C-band, X-band, etc.) of weather radar, and can also be applied to airborne Doppler weather radar. It has strong scalability and applicability, high spatiotemporal resolution and accuracy, and can be continuously monitored 24 hours a day without human intervention. Attached Figure Description

[0041] Figure 1 This is a flowchart of the low-altitude wind shear classification identification and early warning method based on Doppler weather radar according to the present invention.

[0042] Figure 2 This is a schematic diagram of the low elevation angle scanning of the Doppler weather radar of the present invention. Figure 2 Image (a) is a schematic diagram of a single-layer elevation angle scan. Figure 2 (b) is a schematic diagram for extracting the radially increasing divergent shear segment. Figure 2 (c) is a schematic diagram of extracting a convergent shear segment with a monotonically decreasing radial velocity;

[0043] Figure 3 The flowchart for shear segment identification is as follows. Figure 3 (a) is a flowchart of the divergent shear segment identification process. Figure 3 (b) is a flowchart for identifying convergent shear segments;

[0044] Figure 4 This is an example diagram of low-altitude wind shear identification in Example 1. Figure 4 (a) shows the radial velocity data. Figure 4 Figure (b) shows the results of low-level wind shear identification and early warning. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0046] The following is combined with Figures 1-4 This invention describes a method for classifying and identifying low-level wind shear based on Doppler weather radar. This method identifies the intensity level of low-level wind shear based on weather radar data (which can also be extended to airborne meteorological radar data), and is suitable for wind-sensitive areas such as airport runways near meteorological radar and low-altitude flight areas of UAVs.

[0047] Doppler weather radar is the most effective means of detecting precipitation or severe convective weather processes. It can detect wind speed and direction information in the lower atmosphere. New-generation Doppler weather radars have high spatiotemporal resolution. S-band weather radars typically have a temporal and spatial resolution of 6 minutes and 250 meters, while C-band radars can achieve 1 minute and 150 meters. X-band radars have even higher temporal and spatial resolutions, reaching 1 minute and 60 meters. Furthermore, they can conduct continuous all-weather detection, tracking the occurrence, development, dissipation, and movement of small- and medium-scale weather systems in real time. These advantages of Doppler weather radar provide strong support for inverting low-level wind shear characteristics.

[0048] Example 1: This example provides a method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar, such as... Figure 1 As shown, it includes the following steps:

[0049] S1. Based on low-level elevation radial velocity data detected by Doppler weather radar, an adjustable sliding window is set. Using the window threshold method, all velocity shear segments at each azimuth angle are extracted radially from near to far, resulting in convergent shear segments with monotonically decreasing radial velocity and divergent shear segments with monotonically increasing radial velocity. The radial velocity shear magnitude and shear length of each segment are then calculated. Specifically:

[0050] Low-level elevation radial velocity data detected using Doppler weather radar, such as Figure 2 The diagram in (a) shows a Doppler weather radar detection at a low elevation angle (typically 0.5°). The radar emits electromagnetic waves to scan a circle in a predetermined order (typically at 1° intervals; a 360° azimuth scan completes 360 sets of radial data, assuming each set of radial data contains Ng range data). Figure 2 (b) shows a set of radial data. The area within the black solid line box in the figure represents the radial velocity monotonically increasing shear segment (radial wind shear segment). Figure 2 (c) represents another set of radial data. The area within the black solid box in the figure represents the radial velocity monotonically decreasing shear segment region (converging wind shear segment).

[0051] For any radial data segment, an adjustable sliding window (length Nd, default 4) is defined. The sliding window is used sequentially from near to far (from 1 to Ng-Nd) to determine if the data within the window is a monotonically increasing shear segment region (divergent wind shear segment). After multiple windows satisfy the monotonically increasing identification, these regions are defined as shear segment regions. After identifying a shear segment, a shear segment quality check and another monotonically increasing check are performed. Those that pass all checks are saved as monotonically increasing wind shear segments (divergent wind shear segments), denoted as SEGDP. For the detection of monotonically decreasing wind shear segments (convergent wind shear segments), the process is the same, only the increasing check is replaced with a decreasing check, denoted as SEGDN. The identification process is as follows: Figure 3 (a) and Figure 3 As shown in (b), the main process judgment conditions are defined as follows:

[0052] Monotonically increasing condition: For Nd radial velocity data in the window region ,satisfy ;

[0053] Monotonically decreasing condition: For Nd radial velocity data in the window region ,satisfy ;

[0054] Valid data definition: For Nd radial velocity data within a window region ,satisfy All data are valid (invalid radial velocity data detected by radar is generally replaced by 999 or -999).

[0055] Maximum endpoint data determination: radial velocity data at the endpoint of the shear segment. ;

[0056] Minimum endpoint data determination: radial velocity data at the endpoint of the shear segment ;

[0057] Overall shear segment data monotonically increasing detection: First, smooth all data within the shear segment, and then judge the monotonically increasing radial data of the shear segment after data smoothing;

[0058] Overall shear segment data monotonically decreasing detection: First, smooth all data within the shear segment, and then judge the monotonically decreasing radial data of the shear segment after data smoothing.

[0059] Overall shear segment data anomaly detection (monotonically increasing, divergent wind shear): For all radial velocity data within the shear segment, the proportion of data with radial velocity less than the starting point does not exceed Rav, and the proportion of data with radial velocity greater than the ending point does not exceed Rav, where Rav is the defined anomaly proportion threshold (default is 0.125).

[0060] After the shear segment identification is completed, the position information of each shear segment in polar coordinates is recorded (azimuth angle θ, radial starting position Ns, radial ending position Ne, number of data points Nall = Ne - Ns + 1). At the same time, parameters such as radial velocity shear magnitude (ShearMax) and shear length (ShearL) of each shear segment are calculated, as follows:

[0061] Max is the function that retrieves the maximum value, and Min is the function that retrieves the minimum value. This represents all radial velocities within the shear segment.

[0062] ;in This represents the resolution of the radar radial velocity data.

[0063] S2. Using the same low-level elevation angle radial velocity data, calculate the two-dimensional divergence field. Divide the identified two-dimensional divergence field into convergence regions with positive divergence and divergence regions with negative divergence. Then, extract divergence cores according to a set intensity level for the divergence in both types of regions, and obtain the two-dimensional region boundaries of each divergence core; specifically:

[0064] The radial shear calculation method is extended to a two-dimensional divergence calculation method to calculate the divergence of a single-layer radial velocity detected by radar. When a Doppler weather radar uses PPI scanning, the radial velocity is as follows: Figure 2 The polar coordinate distribution centered on the radar shown in (a) uses the following method to calculate the divergence: In the radial velocity field, take 2Nr+1 grid points radially as the length; take 2Ns+1 grid points azimuthally as the width. This region is the two-dimensional divergence region. Using the center coordinates of this region as the origin, the first... The radial velocity of each grid point is Calculate the single-layer radial velocity of radar detection for any number of layers. Two-dimensional divergence at each grid point , can be represented as:

[0065] ;

[0066] in, For follow The coefficients vary depending on the situation. It is the azimuth angle. for Distance to the origin This is a function for validating radial velocity data in a two-dimensional region.

[0067] After processing all radial velocities in a single layer using the above method, the two-dimensional divergence field data in polar coordinates is obtained, denoted as follows: Two-dimensional divergence field data The interpolation value is denoted as Cartesian coordinate data centered at the radar station. ,Right now:

[0068] ;

[0069] in, This is a general two-dimensional data interpolation method, with linear interpolation as the default.

[0070] Using interpolated data The divergence core region (positive divergence core) is classified into two categories: convergence (positive divergence) and divergence (negative divergence). Each category is further divided into five levels based on intensity (the level classification can be adjusted according to user needs). Negative divergence core ), represented as:

[0071] ;

[0072] ;

[0073] For the two-dimensional divergence field core data at the above five levels, a general boundary extraction function is used to extract the set of boundary coordinates for the positive divergence core region. The set of boundary coordinates of the negative divergence core region ,Right now:

[0074] ,

[0075] ;

[0076] in , This is a general function for extracting boundaries in binary data. It's important to note that each level of divergence core may have multiple sub-cores. For example, The boundary set contains multiple closed intervals, each of which satisfies For simplicity, the following procedure is based on the premise that each boundary set contains a single closed interval.

[0077] S3. Match the convergent shear segment with the divergence core of the convergence region to form a convergent wind shear cluster; match the divergent shear segment with the divergence core of the divergence region to form a divergent wind shear cluster; specifically:

[0078] For the identified radial velocity monotonically increasing shear segment region (divergent wind shear segment), each shear segment and the identified positive divergence core are compared. One-to-one matching, with positive divergence core For example, the matching rules are as follows:

[0079] Assume that step S1 identified a total of n divergent wind shear segments. For any divergent wind shear segment First, the position information of each point in the shear segment is converted from polar coordinates (azimuth, distance) to Cartesian coordinates. ,in As long as any coordinate in the shear segment Located in the divergence core boundary coordinates Within this range, the divergent wind shear segment With divergence core Correspondingly, after all divergent wind shear segments have been tested, the correlation with the divergence core can be obtained. A set of divergent wind shear segments that match the divergence core are defined as this set of wind shear segments. Matching wind shear clusters, for Correspondingly, the matching rules for other divergent cores are similar to those described above, resulting in wind shear clusters. The wind shear clusters matched by the convergent divergence core are... .

[0080] S4. For the remaining shear segments that do not match any divergence core, perform two-dimensional clustering based on the shear segment azimuth and radial overlap distance to obtain convergent or divergent shear clusters located in the non-divergence core region; specifically:

[0081] After matching in step S3, there may still be shear segments that fail to match the divergence core. These are defined as monotonically increasing shear segments SEGDPU (divergent wind shear segments) and monotonically decreasing shear segments SEGDNU (convergent wind shear segments) located in non-divergence core regions. SEGGDPU and SEGDNU are clustered separately, with the following main clustering principles: (a) azimuth interval less than 4°; (b) based on principle (a), the radial overlap area of ​​the shear segments is greater than 300m. Shear segments meeting both conditions are clustered into the same shear cluster. The wind shear cluster obtained by clustering monotonically increasing shear segments SEGDPU (divergent wind shear segments) is... , The total number of divergent wind shear clusters obtained from clustering with non-divergent cores. The wind shear clusters matched with convergent divergent cores are... , This represents the total number of convergent wind shear clusters with non-dispersion cores obtained from clustering.

[0082] S5. Extract the wind shear magnitude, region area, and azimuth parameters for each type of shear cluster, and classify each shear cluster accordingly; specifically:

[0083] The radial convergence (divergence) shear clusters obtained in step S3 above, located in the divergence core region, are respectively... and the radial convergence (divergence) shear clusters located in the non-divergence core region obtained in step S4 First, the shear clusters are numbered, then parameters such as wind shear size, area, and azimuth number are extracted for user selection and display. There are two shear classification standards. The first is based on radial velocity wind shear intensity, which can be classified according to the size of the largest actual convergent (divergent) wind shear in each shear cluster. The default is based on the internationally accepted Beaufort Scale (0-12 levels), combined with the "Typhoon Business and Service Regulations" issued by the China Meteorological Administration in 2001, which supplements typhoons above level 12 to level 17 (13-17) using the Beaufort scale, as shown in Table 1. Wind shear is classified as B1 to B17 based on wind force levels 1 to 17. The second is based on two-dimensional divergence intensity field, which is based on ±2×10 -3 S -1The divergent and convergent divergences are graded into P1~P5 and N1~N5 levels; the higher the number, the stronger the wind shear. After grading, the levels are stored in each wind shear cluster for users to select.

[0084] Table 1 Wind Force Level Table

[0085]

[0086] S6. Extract the boundary of the graded shear cluster or the boundary of the divergence core region, generate the warning area using a two-dimensional closed-form fitting method, and output and display it; specifically:

[0087] The system can display corresponding wind shear clusters according to user needs (such as customizing wind shear level display thresholds, area display thresholds, azimuth angle number display thresholds, etc.), and provide early warnings for wind shear level areas that require special attention. The early warning area fitting is calculated in a Cartesian coordinate system. There are two fitting methods. The first method uses the two-dimensional wind shear cluster boundary defined by the actual wind shear intensity according to radial velocity in step S5. That is, it extracts the starting position point of each shear segment in the two-dimensional wind shear cluster and the entire set of coordinate points [x,y] of the shear segment located at the starting azimuth angle of the shear cluster as the boundary fitting. The fitting method adopts the general ellipse fitting formula.

[0088] The second method involves extracting the boundary of the divergence core hierarchical region of the two-dimensional divergence field, i.e., in step S2. and The set of coordinate points [x, y] is fitted using a common ellipse fitting formula.

[0089] The above can be used to fit the minor X-axis (radius), minor Y-axis (radius), tilt angle, center of the tilted ellipse on the X-axis and Y-axis in a Cartesian coordinate system centered on the radar, length of the major axis, and length of the minor axis. If the number of data points is small, the ellipse fitting error will increase. In this case, a circular fitting method is used to obtain the x-coordinate and y-coordinate of the circle's center, as well as the diameter of the circle. Overlaying these graphs onto a map centered on the radar station allows for early warning of key areas of interest.

[0090] Figure 4 This is an example image of low-altitude divergent wind shear identification. Figure 4 (a) shows the radial velocity data at the original low-level elevation angle. Figure 4 (b) shows the identification and early warning results. Figure 4 In (b), the fill color represents divergence, and the magenta lines represent all identified divergent wind shear segments. For ease of display, a single divergence core is selected. The levels are matched and displayed. The blue line segment represents the divergent wind shear segment that matches the divergence core, and the black solid ellipse P2 indicates the use of the divergence core. Elliptical boundary fitted to the grade boundary. The black dashed ellipse B7 is the boundary not fitted to... Wind shear clusters obtained by clustering divergent wind shear segments with divergence core matching (those with wind shear magnitude greater than level 7 and area greater than 1 km²) 2 (Threshold filtering) reveals that the method of this invention can obtain the intensity and location of all wind shear segments and wind shear clusters of different levels, and select, display and warn users based on the level of the objects of interest.

[0091] Example 2: This example provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute a low-altitude wind shear classification and early warning method based on Doppler weather radar. The method includes the following steps:

[0092] S1. Based on the low-level elevation angle radial velocity data detected by Doppler weather radar, an adjustable sliding window is set, and the window threshold method is used to slide and extract all velocity shear segments at each azimuth angle from near to far along the radial direction. This yields convergent shear segments with monotonically decreasing radial velocity and divergent shear segments with monotonically increasing radial velocity. The radial velocity shear magnitude and shear length of each shear segment are then calculated.

[0093] S2. Calculate the two-dimensional divergence field using the same low-level elevation angle radial velocity data. Divide the identified two-dimensional divergence field into convergence regions with positive divergence and divergence regions with negative divergence. Then, extract divergence cores in the two types of regions according to the set intensity classification and obtain the two-dimensional region boundaries of each divergence core.

[0094] S3. Match the convergent shear segment with the divergence core of the convergent region to form a convergent wind shear cluster; match the divergent shear segment with the divergence core of the divergent region to form a divergent wind shear cluster.

[0095] S4. For the remaining shear segments that have not been matched with any divergence core, perform two-dimensional clustering based on the shear segment azimuth angle and radial overlap distance to obtain convergent shear clusters or divergent shear clusters located in the non-divergence core region.

[0096] S5. Extract the wind shear size, region area, and azimuth parameters of each type of shear cluster, and classify each shear cluster.

[0097] S6. Extract the boundary of the graded shear cluster or the boundary of the divergence core region, and use a two-dimensional closed graph fitting method to generate the warning area and output it for display.

[0098] Example 3: This example provides an electronic device that may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory to execute a low-altitude wind shear classification and early warning method based on Doppler weather radar. This method includes the following steps:

[0099] S1. Based on the low-level elevation angle radial velocity data detected by Doppler weather radar, an adjustable sliding window is set, and the window threshold method is used to slide and extract all velocity shear segments at each azimuth angle from near to far along the radial direction. This yields convergent shear segments with monotonically decreasing radial velocity and divergent shear segments with monotonically increasing radial velocity. The radial velocity shear magnitude and shear length of each shear segment are then calculated.

[0100] S2. Calculate the two-dimensional divergence field using the same low-level elevation angle radial velocity data. Divide the identified two-dimensional divergence field into convergence regions with positive divergence and divergence regions with negative divergence. Then, extract divergence cores in the two types of regions according to the set intensity classification and obtain the two-dimensional region boundaries of each divergence core.

[0101] S3. Match the convergent shear segment with the divergence core of the convergent region to form a convergent wind shear cluster; match the divergent shear segment with the divergence core of the divergent region to form a divergent wind shear cluster.

[0102] S4. For the remaining shear segments that have not been matched with any divergence core, perform two-dimensional clustering based on the shear segment azimuth angle and radial overlap distance to obtain convergent shear clusters or divergent shear clusters located in the non-divergence core region.

[0103] S5. Extract the wind shear size, region area, and azimuth parameters of each type of shear cluster, and classify each shear cluster.

[0104] S6. Extract the boundary of the graded shear cluster or the boundary of the divergence core region, and use a two-dimensional closed graph fitting method to generate the warning area and output it for display.

[0105] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0106] Example 4: This example provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a low-altitude wind shear classification and early warning method based on Doppler weather radar. The method includes the following steps:

[0107] S1. Based on the low-level elevation angle radial velocity data detected by Doppler weather radar, an adjustable sliding window is set, and the window threshold method is used to slide and extract all velocity shear segments at each azimuth angle from near to far along the radial direction. This yields convergent shear segments with monotonically decreasing radial velocity and divergent shear segments with monotonically increasing radial velocity. The radial velocity shear magnitude and shear length of each shear segment are then calculated.

[0108] S2. Calculate the two-dimensional divergence field using the same low-level elevation angle radial velocity data. Divide the identified two-dimensional divergence field into convergence regions with positive divergence and divergence regions with negative divergence. Then, extract divergence cores in the two types of regions according to the set intensity classification and obtain the two-dimensional region boundaries of each divergence core.

[0109] S3. Match the convergent shear segment with the divergence core of the convergent region to form a convergent wind shear cluster; match the divergent shear segment with the divergence core of the divergent region to form a divergent wind shear cluster.

[0110] S4. For the remaining shear segments that have not been matched with any divergence core, perform two-dimensional clustering based on the shear segment azimuth angle and radial overlap distance to obtain convergent shear clusters or divergent shear clusters located in the non-divergence core region.

[0111] S5. Extract the wind shear size, region area, and azimuth parameters of each type of shear cluster, and classify each shear cluster.

[0112] S6. Extract the boundary of the graded shear cluster or the boundary of the divergence core region, and use a two-dimensional closed graph fitting method to generate the warning area and output it for display.

[0113] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar, characterized in that, Includes the following steps: S1. Based on the low-level elevation angle radial velocity data detected by Doppler weather radar, an adjustable sliding window is set, and the window threshold method is used to slide and extract all velocity shear segments at each azimuth angle from near to far along the radial direction. This yields convergent shear segments with monotonically decreasing radial velocity and divergent shear segments with monotonically increasing radial velocity. The radial velocity shear magnitude and shear length of each shear segment are then calculated. S2. Using the same low-level elevation angle radial velocity data, calculate the two-dimensional divergence field. Divide the identified two-dimensional divergence field into convergence regions with positive divergence and divergence regions with negative divergence. Then, extract divergence cores according to a set intensity level for the divergence in both types of regions, and obtain the two-dimensional region boundaries of each divergence core; specifically: Low-level elevation radial velocity data detected by Doppler weather radar are processed in a polar coordinate system. In this system, a certain number of grid points are taken in the radial direction as the length, and a certain number of grid points are taken in the azimuth direction as the width. This region is the two-dimensional divergence region. The origin is taken as the coordinates of the center of this region. The radial velocity of each grid point is Calculate the single-layer radial velocity of radar detection for any number of layers. Two-dimensional divergence at each grid point , can be represented as: ； in, For follow The coefficients vary depending on the type of coefficient. It is the azimuth angle. for Distance to the origin This is a function for validating radial velocity data in a two-dimensional region. After processing all radial velocities in a single layer, the two-dimensional divergence field data in polar coordinates is obtained, denoted as . Two-dimensional divergence field data The interpolation value is denoted as Cartesian coordinate data centered at the radar station. ,Right now: ； in, This is a general two-dimensional data interpolation method, with linear interpolation as the default. Using interpolated data The regions are classified into two categories: convergence regions with positive divergence and divergence regions with negative divergence. Each category is further divided into five levels of divergence core regions based on intensity, namely, positive divergence cores. and negative divergence core , is represented as: ； ； For the two-dimensional divergence field core data at the above five levels, a general boundary extraction function is used to extract the set of boundary coordinates for the positive divergence core region. The set of boundary coordinates of the negative divergence core region ,Right now: ， ； in , This is a general function for extracting boundaries from binary data; S3. Match the convergent shear segment with the divergence core of the convergent region to form a convergent wind shear cluster; match the divergent shear segment with the divergence core of the divergent region to form a divergent wind shear cluster. S4. For the remaining shear segments that have not been matched with any divergence core, perform two-dimensional clustering based on the shear segment azimuth angle and radial overlap distance to obtain convergent shear clusters or divergent shear clusters located in the non-divergence core region. S5. Extract the wind shear size, region area, and azimuth parameters of each type of shear cluster, and classify each shear cluster. S6. Extract the boundary of the graded shear cluster or the boundary of the divergence core region, and use a two-dimensional closed graph fitting method to generate the warning area and output it for display.

2. The method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar according to claim 1, characterized in that, Step S1 is as follows: Low-level elevation radial velocity data detected by Doppler weather radar are processed in polar coordinates, and the acquired data is first subjected to quality control. Based on the radar resolution, a sliding window of length N is defined and slids radially from near to far at each azimuth angle. It is determined whether the data within the window is a shear segment with a monotonically increasing or decreasing radial velocity. After multiple windows are identified as monotonically increasing or decreasing, these regions are defined as shear segment regions. Among them, the radial velocity monotonically decreasing shear segment region is a convergent shear segment, and the radial velocity monotonically increasing shear segment region is a divergent shear segment. Obtain the position information of all shear segments in polar coordinates at each azimuth angle, and calculate the radial velocity shear magnitude and shear length of each shear segment; remove shear segments that do not meet the conditions, and store the two types of shear segments separately by number.

3. The method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar according to claim 1, characterized in that, Step S3 is as follows: The position information of the convergent shear segment and the divergent shear segment obtained in step S1 is transformed from the polar coordinate system to the Cartesian coordinate system; Using the shear segment-divergence intensity center closed region position overlap matching method, the convergent shear segments are matched one by one with the divergence core of the convergence region obtained in step S2, and the convergent shear segments matched with the same divergence core of the convergence region are clustered into convergent wind shear clusters. The divergent shear segments are matched one by one with the divergence cores of the divergence region obtained in step S2, and the divergent shear segments that match the same divergence core of the divergence region are clustered into divergent wind shear clusters.

4. The method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar according to claim 1, characterized in that, Step S4 is as follows: After the matching in step S3, convergent wind shear segments or divergent shear segments that fail to match any convergence or divergence core are subjected to cyclic two-dimensional clustering according to the shear segment azimuth threshold standard and radial overlap distance standard until all shear segments have been clustered and matched. Finally, convergent shear clusters located in non-divergence core regions and divergent shear clusters located in non-divergence core regions are obtained.

5. The method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar according to claim 1, characterized in that, Step S5 is as follows: Based on the convergent and divergent wind shear clusters obtained in step S3, and the convergent shear clusters in the non-divergent core region and the divergent shear clusters in the non-divergent core region obtained in step S4, after numbering, the wind shear magnitude, region area and azimuth number parameters of each shear cluster are extracted, and the classification is carried out according to the radial velocity wind shear intensity or according to the two-dimensional divergence intensity field.

6. The method for graded identification and early warning of low-altitude wind shear based on Doppler weather radar according to claim 5, characterized in that, Step S6 is as follows: For shear clusters classified according to radial velocity wind shear intensity, the starting position point of each shear segment in each shear cluster and the entire coordinate point of the shear segment located at the starting azimuth angle of the shear cluster are extracted as boundaries, and a two-dimensional closed graph is used for fitting. For shear clusters graded according to a two-dimensional divergence intensity field, the boundaries of the divergence core grading regions of the two-dimensional divergence field are extracted and fitted using a two-dimensional closed graph.

7. A non-transitory computer-readable storage medium, characterized in that, It stores computer instructions that cause the computer to execute the low-altitude wind shear classification and early warning method based on Doppler weather radar as described in any one of claims 1-6.

8. An electronic device, characterized in that, include: The system includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor calls logical instructions from the memory to execute the low-altitude wind shear classification and early warning method based on Doppler weather radar as described in any one of claims 1-6.

9. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer performs the low-altitude wind shear classification identification and early warning method based on Doppler weather radar as described in any one of claims 1-6.

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